{"id":32993,"date":"2020-01-28T19:30:03","date_gmt":"2020-01-29T00:30:03","guid":{"rendered":"https:\/\/ahrefs.com\/blog\/?p=32993"},"modified":"2024-10-17T10:55:00","modified_gmt":"2024-10-17T15:55:00","slug":"lsi-keywords","status":"publish","type":"post","link":"https:\/\/ahrefs.com\/blog\/lsi-keywords\/","title":{"rendered":"LSI Keywords: What are They and Do They Matter?"},"content":{"rendered":"<div class=\"intro-txt\"> People say that LSI keywords have the power to boost Google rankings. Is this true, or is it yet another SEO&nbsp;myth?&nbsp;<\/div>\n<p>Read almost any article about LSI keywords, and you\u2019ll be told two things:<\/p>\n<ol>\n<li>Google uses a technology called LSI to index web&nbsp;pages.<\/li>\n<li>Using LSI keywords in your content helps you <a href=\"https:\/\/ahrefs.com\/blog\/how-to-rank-higher-on-google\/\">rank higher on Google<\/a>.<\/li>\n<\/ol>\n<p><strong>Both of these claims are technically false.<\/strong><\/p>\n<p>In this guide, you\u2019ll learn why that is and what to do about&nbsp;it.<\/p>\n<p>But first, the basics\u2026<\/p>\n<div class=\"hub-link\"><img decoding=\"async\" alt=\"Beginner's guide to on-page SEO\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/svg\/3.svg\"><div class=\"hl-title\">New to on-page SEO? Check out&nbsp;our<\/div><div class=\"hl-content\"><a href=\"https:\/\/ahrefs.com\/blog\/on-page-seo\/\" target=\"_blank\">Beginner\u2019s guide to on-page SEO<\/a><\/div><\/div>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"What are LSI keywords?\" data-section=\"what-are-lsi-keywords\">\n<h2>What are LSI keywords?<\/h2>\n<\/div><\/div>\n<p>LSI keywords are words and phrases that Google sees as semantically-related to a topic\u2014<strong>at least according to many in the SEO community<\/strong>. If you\u2019re talking about cars, then LSI keywords might&nbsp;be <em>automobile<\/em>, <em>engine<\/em>, <em>road<\/em>, <em>tires<\/em>, <em>vehicle<\/em>, and <em>automatic transmission<\/em>.<\/p>\n<p>But, according to Google\u2019s John Mueller, LSI keywords don\u2019t&nbsp;exist:<\/p>\n<blockquote class=\"twitter-tweet\" data-conversation=\"none\">\n<p dir=\"ltr\" lang=\"en\">There\u2019s no such thing as LSI keywords -- anyone who\u2019s telling you otherwise is mistaken, sorry.\u2014  John  (@JohnMu) <a href=\"https:\/\/twitter.com\/JohnMu\/status\/1156293862681468929?ref_src=twsrc%5Etfw\">July 30,&nbsp;2019<\/a><\/p>\n<\/blockquote>\n<p><script async src=\"https:\/\/platform.twitter.com\/widgets.js\" charset=\"utf-8\"><\/script><\/p>\n<p>So what\u2019s the deal&nbsp;here?<\/p>\n<p>Before we answer that question, we first need to understand a bit more about LSI itself.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"What is Latent Semantic Indexing (LSI)?\" data-section=\"what-is-latent-semantic-indexing\">\n<h2>What is Latent Semantic Indexing (LSI)?<\/h2>\n<\/div><\/div>\n<p>Latent Semantic Indexing (LSI), or Latent Semantic Analysis (LSA), is a natural-language processing technique developed in the&nbsp;1980s.<\/p>\n<p>Unfortunately, unless you\u2019re familiar with mathematical concepts like <em>eigenvalues<\/em>, <em>vectors<\/em>, and <em>single value decomposition<\/em>, the technology itself isn\u2019t that easy to understand.<\/p>\n<p>For that reason, we won\u2019t be tackling how LSI&nbsp;works.<\/p>\n<p>Instead, we\u2019ll focus on the problem it was created to&nbsp;solve.<\/p>\n<p>Here\u2019s how the creators of LSI define this problem:<\/p>\n<blockquote><p>The words a searcher uses are often not the same as those by which the information sought has been indexed.<\/p><\/blockquote>\n<p>But what does this actually mean?<\/p>\n<p>Say that you want to know when summer ends and fall begins. Your WiFi is down, so you go old school and grab an encyclopedia. Instead of randomly flicking through thousands of pages, you lookup \u201cfall\u201d in the index and flick to the right&nbsp;page.<\/p>\n<p>Here\u2019s what you&nbsp;see:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"785\" class=\"wp-image-32982\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-down-1.png\" alt=\"lsi keywords fall down 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-down-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-down-1-487x425.png 487w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-down-1-768x670.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>Clearly, that\u2019s not the type of fall you wanted to learn&nbsp;about.<\/p>\n<p>Not one to be defeated that easily, you flick back and realize that what you\u2019re looking for is indexed under \u201cautumn\u201d\u2014another name for&nbsp;fall.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"785\" class=\"wp-image-32975\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-season-1.png\" alt=\"lsi keywords fall season 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-season-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-season-1-487x425.png 487w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/lsi-keywords-fall-season-1-768x670.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>The problem here is that \u201cfall\u201d is a synonym and polysemic word.<\/p>\n<h3>What are synonyms?<\/h3>\n<p>Synonyms are words or phrases that mean the same or nearly the same thing as another word or phrase.<\/p>\n<p>Examples include <em>rich<\/em>&nbsp;and <em>wealthy<\/em>, <em>fall<\/em>&nbsp;and <em>autumn<\/em>, and <em>cars <\/em>and <em>automobiles<\/em>.<\/p>\n<p>Here\u2019s why synonyms are problematic, according to the LSI patent:<\/p>\n<blockquote>[\u2026] there is a tremendous diversity in the words people use to describe the same object or concept; this is called synonymy. Users in different contexts, or with different needs, knowledge or linguistic habits will describe the same information using different terms. For example, it has been demonstrated that any two people choose the same main keyword for a single, well-known object less than 20% of the time on average.<\/blockquote>\n<p>But how does this relate to search engines?<\/p>\n<p>Imagine that we have two web pages about cars. Both are identical, but one substitutes all instances of the word&nbsp;<em>cars<\/em>&nbsp;for <em>automobiles<\/em>.<\/p>\n<p>If we were to use a primitive search engine that only indexes the words and phrases on the page, it would only return one of these pages for the query \u201ccars.\u201d<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"825\" class=\"wp-image-32990\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/car-automobile-synonym-1.png\" alt=\"car automobile synonym 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/car-automobile-synonym-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/car-automobile-synonym-1-464x425.png 464w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/car-automobile-synonym-1-768x704.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>This is bad because both results are relevant; it\u2019s just that one describes what we\u2019re looking for in a different way. The page that uses the word <em>automobile<\/em>&nbsp;instead of <em>cars<\/em>&nbsp;might even be the better result.<\/p>\n<p>Bottom line: search engines need to understand synonyms to return the best results.<\/p>\n<h3>What are polysemic words?<\/h3>\n<p>Polysemic words and phrases are those with multiple different meanings.<\/p>\n<p>Examples include mouse (rodent \/ computer), bank (financial institute \/ riverbank), and bright (light \/ intelligent).<\/p>\n<p>Here\u2019s why these cause problems, according to the creators of&nbsp;LSI:<\/p>\n<blockquote><p>In different contexts or when used by different people the same word takes on varying referential significance (e.g., \u201cbank\u201d in river bank versus \u201cbank\u201d in a savings bank). Thus the use of a term in a search query does not necessarily mean that a text object containing or labeled by the same term is of interest.<\/p><\/blockquote>\n<p>These words present search engines with a similar problem to synonyms.<\/p>\n<p>For example, say that we search for \u201capple computer.\u201d Our primitive search engine might return both of these pages, even though one is clearly not what we\u2019re looking for:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"747\" class=\"wp-image-32974\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/apple-computer-polysemic-1.png\" alt=\"apple computer polysemic 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/apple-computer-polysemic-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/apple-computer-polysemic-1-512x425.png 512w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/apple-computer-polysemic-1-768x637.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>Bottom line: search engines that don\u2019t understand the different meanings of polysemic words are likely to return irrelevant results.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"How does LSI work?\" data-section=\"how-does-lsi-work\">\n<h2>How does LSI&nbsp;work?<\/h2>\n<\/div><\/div>\n<p>Computers are&nbsp;dumb.<\/p>\n<p>They don\u2019t have the inherent understanding of word relationships that we humans do.<\/p>\n<p>For example, everyone knows that <em>big<\/em>&nbsp;and <em>large<\/em>&nbsp;mean the same thing. And everyone knows that John Lennon was in The Beatles.<\/p>\n<p>But a computer doesn\u2019t have this knowledge without being&nbsp;told.<\/p>\n<p>The problem is that there\u2019s no way to tell a computer everything. It would just take too much time and effort.<\/p>\n<p>LSI solves this problem by using complex mathematical formulas to derive the relationships between words and phrases from a set of documents.<\/p>\n<p>In simple terms, if we run LSA on a set of documents about seasons, the computer can likely figure out a few things:<\/p>\n<p>First, the word <em>fall<\/em>&nbsp;is synonymous with <em>autumn<\/em>:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"937\" class=\"wp-image-32972\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/fall-autumn-1.png\" alt=\"fall autumn 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/fall-autumn-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/fall-autumn-1-408x425.png 408w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/fall-autumn-1-768x800.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>Second, words like <em>season<\/em>, <em>summer<\/em>, <em>winter<\/em>, <em>fall<\/em>, and <em>spring<\/em>&nbsp;are all semantically related:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"937\" class=\"wp-image-32977\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/semantically-related-words-1.png\" alt=\"semantically related words 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/semantically-related-words-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/semantically-related-words-1-408x425.png 408w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/semantically-related-words-1-768x800.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>Third, <em>fall<\/em>&nbsp;is semantically-related to two different sets of&nbsp;words:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"937\" class=\"wp-image-32987\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/polysemic-fall-1.png\" alt=\"polysemic fall 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/polysemic-fall-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/polysemic-fall-1-408x425.png 408w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/polysemic-fall-1-768x800.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>Search engines can then use this information to go beyond exact-query matching and deliver more relevant search results.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"1111\" class=\"wp-image-32976\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/search-engine-relevant-result-1.png\" alt=\"search engine relevant result 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/search-engine-relevant-result-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/search-engine-relevant-result-1-344x425.png 344w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/search-engine-relevant-result-1-768x948.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"Does Google Use LSI?\" data-section=\"does-google-use-lsi\">\n<h2>Does Google use&nbsp;LSI?<\/h2>\n<\/div><\/div>\n<p>Given the problems LSI solves, it\u2019s easy to see why people assume Google uses LSI technology. After all, it\u2019s clear that matching exact queries is an unreliable way for search engines to return relevant documents.<\/p>\n<p>Plus, we see evidence every day that Google understands synonymy:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"685\" class=\"wp-image-32983\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/1-rich-knowledge-graph-1.jpg\" alt=\"1 rich knowledge graph 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/1-rich-knowledge-graph-1.jpg 1600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/1-rich-knowledge-graph-1-680x291.jpg 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/1-rich-knowledge-graph-1-768x329.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/1-rich-knowledge-graph-1-1536x658.jpg 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><\/p>\n<p>And polysemy:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"616\" class=\"wp-image-32973\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/2-mouse-knowledge-graph-1.png\" alt=\"2 mouse knowledge graph 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/2-mouse-knowledge-graph-1.png 1600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/2-mouse-knowledge-graph-1-680x262.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/2-mouse-knowledge-graph-1-768x296.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/2-mouse-knowledge-graph-1-1536x591.png 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><\/p>\n<p>But despite this, <strong>Google almost certainly doesn\u2019t use LSI technology<\/strong>.<\/p>\n<p>How do we know? Google representatives say&nbsp;so.<\/p>\n<p>Don\u2019t believe them?<\/p>\n<p>Here are three more pieces of evidence to back up this&nbsp;fact:<\/p>\n<h3>1. LSI is old technology<\/h3>\n<p>LSI was invented in the 1980s <em>before<\/em>&nbsp;the creation of the World Wide Web. As such, it was never intended to be applied to such a large set of documents.<\/p>\n<p>That\u2019s why Google has since developed better, more scalable technology to solve the same problems.<\/p>\n<p>Bill Slawski <a href=\"http:\/\/www.seobythesea.com\/2018\/01\/google-use-latent-semantic-indexing\/\" target=\"_blank\" rel=\"noopener noreferrer\">puts it best<\/a>:<\/p>\n<blockquote><p>LSI technology wasn\u2019t created for anything the size of the Web [\u2026] Google has developed a word vector approach (used for Rankbrain) which is much more modern, scales much better, and works on the Web. Using LSI when you have Word2vec available would be like racing a Ferrari with a go-cart.<\/p><\/blockquote>\n<h3>2. LSI was created to index known document collections<\/h3>\n<p>The World Wide Web is not only large but also dynamic.<\/p>\n<p>This means that the billions of pages in Google\u2019s index change regularly.<\/p>\n<p>That\u2019s a problem because the LSI patent tells us that the analysis needs to run \u201ceach time there is a significant update in the storage files.\u201d<\/p>\n<p>That would take a lot of processing power.<\/p>\n<h3>3. LSI is a patented technology<\/h3>\n<p>The <a href=\"http:\/\/patft.uspto.gov\/netacgi\/nph-Parser?Sect1=PTO1&amp;Sect2=HITOFF&amp;d=PALL&amp;p=1&amp;u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&amp;r=1&amp;f=G&amp;l=50&amp;s1=4,839,853.PN.&amp;OS=PN\/4,839,853&amp;RS=PN\/4,839,853\" target=\"_blank\" rel=\"noopener noreferrer\">Latent Semantic Indexing (LSI) patent<\/a>&nbsp;was granted to Bell Communications Research, Inc. in 1989. Susan Dumais, one of the co-inventors who worked on the technology, later joined Microsoft in 1997, where she worked on search-related innovations.<\/p>\n<p>That said, US patents expire after 20 years, which means that the LSI patent expired in&nbsp;2008.<\/p>\n<p>Given that Google was pretty good at understanding language and returning relevant results much earlier than 2008, this is yet another piece of evidence to suggest that Google doesn\u2019t use&nbsp;LSI.<\/p>\n<p>Once again, Bill Slawski puts it&nbsp;best:<\/p>\n<blockquote><p>Google does attempt to index synonyms and other meanings for words. <strong>But it isn\u2019t using LSI technology to do that.<\/strong>&nbsp;Calling it LSI is misleading people. Google has been offering synonym substitutions and query refinements based upon synonyms since at least 2003, but that doesn\u2019t mean that they are using LSI. It would be like saying that you are using a smart telegraph device to connect to the mobile web.<\/p><\/blockquote>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"Can related words boost rankings?\" data-section=\"can-related-words-boost-rankings\">\n<h2>Can mentioning related words, phrases, and entities boost rankings?<\/h2>\n<\/div><\/div>\n<p>Most SEOs see \u201cLSI keywords\u201d as nothing more than related words, phrases, and entities.<\/p>\n<p>If we roll with that definition\u2014despite it being technically inaccurate\u2014then yes, using some related words and phrases in your content can almost certainly help <a href=\"https:\/\/ahrefs.com\/blog\/how-to-improve-seo\/\">improve SEO<\/a>.<\/p>\n<p>How do we know? Google indirectly tells us so <a href=\"https:\/\/www.google.com\/search\/howsearchworks\/algorithms\/\" target=\"_blank\" rel=\"noopener noreferrer\">here<\/a>:<\/p>\n<blockquote><p>Just think: when you search for \u2018dogs\u2019, you probably don\u2019t want a page with the word \u2018dogs\u2019 on it hundreds of times. With that in mind, algorithms assess if a page contains other relevant content beyond the keyword \u2018dogs\u2019 \u2013 such as pictures of dogs, videos or <strong>even a list of breeds<\/strong>.<\/p><\/blockquote>\n<p>On a page about dogs, Google sees names&nbsp;of individual breeds as semantically related.<\/p>\n<p>But why do these help pages to rank for relevant terms?<\/p>\n<p>Simple: Because they help Google understand the overall topic of the&nbsp;page.<\/p>\n<p>For example, here are two pages that each mention the word \u201cdogs\u201d the same number of&nbsp;times:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"900\" height=\"743\" class=\"wp-image-32992\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/cats-dogs-1.png\" alt=\"cats dogs 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/cats-dogs-1.png 900w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/cats-dogs-1-515x425.png 515w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/cats-dogs-1-768x634.png 768w\" sizes=\"auto, (max-width: 900px) 100vw, 900px\"><\/p>\n<p>Looking at other important words and phrases on each page tells us that only the first is about dogs. The second is mostly about&nbsp;cats.<\/p>\n<p>Google uses this information to rank relevant pages for relevant queries.<\/p>\n<div class=\"post-nav-link clearfix\" id=\"section1\"><a class=\"subhead-anchor\" data-tip=\"tooltip__copielink\" rel=\"#section1\"><svg width=\"19\" height=\"19\" viewBox=\"0 0 14 14\" style><g fill=\"none\" fill-rule=\"evenodd\"><path d=\"M0 0h14v14H0z\" \/><path d=\"M7.45 9.887l-1.62 1.621c-.92.92-2.418.92-3.338 0a2.364 2.364 0 0 1 0-3.339l1.62-1.62-1.273-1.272-1.62 1.62a4.161 4.161 0 1 0 5.885 5.884l1.62-1.62L7.45 9.886zM5.527 5.135L7.17 3.492c.92-.92 2.418-.92 3.339 0 .92.92.92 2.418 0 3.339L8.866 8.473l1.272 1.273 1.644-1.643A4.161 4.161 0 1 0 5.897 2.22L4.254 3.863l1.272 1.272zm-.66 3.998a.749.749 0 0 1 0-1.06l2.208-2.206a.749.749 0 1 1 1.06 1.06L5.928 9.133a.75.75 0 0 1-1.061 0z\" style \/><\/g><\/svg><\/a><div class=\"link-text\" data-anchor=\"How to find related words and phrases\" data-section=\"how-to-find-related-words\">\n<h2>How to find and use related words and phrases<\/h2>\n<\/div><\/div>\n<p>If you\u2019re knowledgeable about a topic, you\u2019ll <strong>naturally<\/strong>&nbsp;include related words and phrases in your content.<\/p>\n<p>For example, it would be difficult to write about the best video games without mentioning words and phrases like \u201cPS4 games,\u201d \u201cCall of Duty,\u201d and \u201cFallout.\u201d<\/p>\n<p>But it\u2019s easy to miss important ones\u2014especially with more complex topics.<\/p>\n<p>For instance, <a href=\"https:\/\/ahrefs.com\/blog\/nofollow-links\/\">our guide to nofollow links<\/a>&nbsp;fails to mention anything about the sponsored and UGC link attributes:<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1558\" height=\"1197\" class=\"wp-image-32981\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/4-nofolloow-post-1.jpg\" alt=\"4 nofolloow post 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/4-nofolloow-post-1.jpg 1558w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/4-nofolloow-post-1-553x425.jpg 553w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/4-nofolloow-post-1-768x590.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/4-nofolloow-post-1-1536x1180.jpg 1536w\" sizes=\"auto, (max-width: 1558px) 100vw, 1558px\"><\/p>\n<p>Google likely sees these as important, semantically-related terms that any good article about the topic should mention.<\/p>\n<p>That may be part of the reason why articles that talk about these things outrank us.<\/p>\n<p>With this in mind, here are nine ways to find potentially related words, phrases, and entities:<\/p>\n<h3>1. Use common sense<\/h3>\n<p>Check your pages to see if you\u2019ve missed any obvious points.<\/p>\n<p>For example, if the page is a biographical article about Donald Trump and doesn\u2019t mention his impeachment, it\u2019s probably worth adding a section about&nbsp;that.<\/p>\n<p>In doing so, you\u2019ll naturally mention related words, phrases, and entities like \u201cMueller Report,\u201d \u201cNancy Pelosi,\u201d and \u201cwhistleblower.\u201d<\/p>\n<div class=\"sidenote\"><div class=\"sidenote-title\">Sidenote.<\/div> Just remember that there\u2019s no way to know for sure&nbsp;whether Google sees these words and phrases as semantically-related. However, as Google aims to understand the relationships between words and entities that we humans inherently understand, there\u2019s something to be said for using common sense.<\/div>\n<h3>2. Look at autocomplete results<\/h3>\n<p>Autocomplete results don\u2019t always show important related keywords, but they can give clues about ones that might be worth mentioning.<\/p>\n<p>For example, we see \u201cdonald trump <strong>spouse<\/strong>,\u201d \u201cdonald trump <strong>age<\/strong>,\u201d and \u201cdonald trump <strong>twitter<\/strong>\u201d as autocomplete results for \u201cdonald trump.\u201d<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1590\" height=\"892\" class=\"wp-image-32985\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/5-autocomplete-1-1.png\" alt=\"5 autocomplete 1 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/5-autocomplete-1-1.png 1590w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/5-autocomplete-1-1-680x381.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/5-autocomplete-1-1-768x431.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/5-autocomplete-1-1-1536x862.png 1536w\" sizes=\"auto, (max-width: 1590px) 100vw, 1590px\"><\/p>\n<p>These aren\u2019t related keywords in themselves, but the people and things they\u2019re referring to might be. In this case, those are <em>Melania Trump<\/em>, <em>73 years old<\/em>, and <em>@realDonaldTrump<\/em>.<\/p>\n<p>Probably all things that should be mentioned in a biographical article, right?<\/p>\n<h3>3. Look at related searches<\/h3>\n<p>Related searches appear at the bottom of the search results.<\/p>\n<p>Like autocomplete results, they can give <strong>clues<\/strong>&nbsp;about potentially related words, phrases, and entities worth mentioning.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"764\" height=\"322\" class=\"wp-image-32980\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-20-at-21.56.57-1.png\" alt=\"Screenshot 2020 01 20 at 21.56.57 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-20-at-21.56.57-1.png 764w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/Screenshot-2020-01-20-at-21.56.57-1-680x287.png 680w\" sizes=\"auto, (max-width: 764px) 100vw, 764px\"><\/p>\n<p>Here, \u201cdonald trump education\u201d is referring to <em>The Wharton School of the University of Pennsylvania<\/em>&nbsp;that he attended.<\/p>\n<h3>4. Use an \u201cLSI keyword\u201d tool<\/h3>\n<p>Popular \u201cLSI keyword\u201d generators have <strong>nothing<\/strong>&nbsp;to do with LSI. However, they do occasionally kick back some useful ideas.<\/p>\n<p>For example, if we plug \u201cdonald trump\u201d into a popular tool, it pulls related people (entities) like his spouse, Melania Trump, and son, Barron Trump.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1260\" height=\"608\" class=\"wp-image-32991\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/7-lsi-tool-1.png\" alt=\"7 lsi tool 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/7-lsi-tool-1.png 1260w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/7-lsi-tool-1-680x328.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/7-lsi-tool-1-768x371.png 768w\" sizes=\"auto, (max-width: 1260px) 100vw, 1260px\"><\/p>\n<h3>5. Look at other keywords the top pages rank&nbsp;for<\/h3>\n<p>Use the \u201cAlso rank for\u201d keyword ideas report in <a href=\"https:\/\/ahrefs.com\/keywords-explorer\">Ahrefs\u2019 Keywords Explorer<\/a>&nbsp;to find potentially related words, phrases, and entities.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"796\" height=\"1334\" class=\"wp-image-32988\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/8-also-rank-for-1.png\" alt=\"8 also rank for 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/8-also-rank-for-1.png 796w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/8-also-rank-for-1-254x425.png 254w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/8-also-rank-for-1-768x1287.png 768w\" sizes=\"auto, (max-width: 796px) 100vw, 796px\"><\/p>\n<p>If there are too many to handle, <a href=\"https:\/\/ahrefs.com\/blog\/content-gap-analysis\/\">try running a Content Gap analysis<\/a> using three of the top-ranking pages, then set the number of intersections to \u201c3.\u201d<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-33002\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/content-gap.png\" alt width=\"892\" height=\"1108\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/content-gap.png 892w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/content-gap-342x425.png 342w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/content-gap-768x954.png 768w\" sizes=\"auto, (max-width: 892px) 100vw, 892px\"><\/p>\n<p>This shows keywords that all of the pages rank for, which often gives you a more refined list of related words and phrases.<\/p>\n<h3>6. Run a TF*IDF analysis<\/h3>\n<p>TF-IDF has <strong>nothing<\/strong>&nbsp;to do with latent-semantic indexing (LSI) or latent-semantic analysis (LSA), but it can occasionally help uncover \u201cmissing\u201d words, phrases, and entities.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"495\" class=\"wp-image-32984\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/9-tf-idf-1.png\" alt=\"9 tf idf 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/9-tf-idf-1.png 1600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/9-tf-idf-1-680x210.png 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/9-tf-idf-1-768x238.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/9-tf-idf-1-1536x475.png 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><\/p>\n<h3>7. Look at knowledge bases<\/h3>\n<p>Knowledge bases like <a href=\"https:\/\/www.wikidata.org\/wiki\/Wikidata:Main_Page\" target=\"_blank\" rel=\"noopener noreferrer\">Wikidata.org<\/a>&nbsp;and <a href=\"https:\/\/www.wikipedia.org\/\" target=\"_blank\" rel=\"noopener noreferrer\">Wikipedia<\/a>&nbsp;are fantastic sources of related terms.<\/p>\n<p>Google also pulls knowledge graph data from these two knowledge bases.<br>\n<img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"1290\" class=\"wp-image-32989\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/12-knowledge-base-1.jpg\" alt=\"12 knowledge base 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/12-knowledge-base-1.jpg 1600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/12-knowledge-base-1-527x425.jpg 527w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/12-knowledge-base-1-768x619.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/12-knowledge-base-1-1536x1238.jpg 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><\/p>\n<h3>8. Reverse-engineer the knowledge graph<\/h3>\n<p>Google stores the relationships between lots of people, things and concepts in something called a <a href=\"https:\/\/ahrefs.com\/blog\/google-knowledge-graph\/\">knowledge graph<\/a>. Results from the knowledge graph often show up in Google search results.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1598\" height=\"1600\" class=\"wp-image-32986\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/13-knowledge-graph-1.jpg\" alt=\"13 knowledge graph 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/13-knowledge-graph-1.jpg 1598w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/13-knowledge-graph-1-424x425.jpg 424w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/13-knowledge-graph-1-768x769.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/13-knowledge-graph-1-1534x1536.jpg 1534w\" sizes=\"auto, (max-width: 1598px) 100vw, 1598px\"><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"600\" class=\"wp-image-32979\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/14-knowledge-graph-2-1.jpg\" alt=\"14 knowledge graph 2 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/14-knowledge-graph-2-1.jpg 1600w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/14-knowledge-graph-2-1-680x255.jpg 680w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/14-knowledge-graph-2-1-768x288.jpg 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/14-knowledge-graph-2-1-1536x576.jpg 1536w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\"><\/p>\n<p>Try searching for your keyword and see if any data from the knowledge graph shows&nbsp;up.<\/p>\n<p>Because these are entities and data points that Google associates with the topic,&nbsp;it\u2019s definitely worth talking about <strong>relevant<\/strong>&nbsp;ones where it makes&nbsp;sense.<\/p>\n<h3>9. Use Google\u2019s Natural Language API to find entities<\/h3>\n<p>Paste the text from a top-ranking page into <a href=\"https:\/\/cloud.google.com\/natural-language\/\" target=\"_blank\" rel=\"noopener noreferrer\">Google\u2019s Natural Language API demo<\/a>. Look for relevant and potentially important entities that you might have missed.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" width=\"1288\" height=\"1600\" class=\"wp-image-32978\" src=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/15-natural-language-api-1.png\" alt=\"15 natural language api 1\" srcset=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/15-natural-language-api-1.png 1288w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/15-natural-language-api-1-342x425.png 342w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/15-natural-language-api-1-768x954.png 768w, https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/15-natural-language-api-1-1236x1536.png 1236w\" sizes=\"auto, (max-width: 1288px) 100vw, 1288px\"><\/p>\n<h2>Final thoughts<\/h2>\n<p>LSI keywords don\u2019t exist, but semantically-related words, phrases, and entities do, and they have the power to boost rankings.<\/p>\n<p>Just make sure to use them where it makes sense, and not to haphazardly sprinkle them whenever and wherever.<\/p>\n<p>In some cases, this may mean adding new sections to your&nbsp;page.<\/p>\n<p>For instance, if you want to add words and entities like \u201cimpeachment\u201d and \u201cHouse Intelligence Committee\u201d to an article about Donald Trump, that\u2019s probably going to require a couple of new paragraphs under a new subheading.<\/p>\n<p>Do you have any other questions about LSI keywords?<\/p>\n<p>Leave a comment or ping me <a href=\"https:\/\/twitter.com\/joshuachardwick?lang=en\" target=\"_blank\" rel=\"noopener noreferrer\">on Twitter<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Read almost any article about LSI keywords, and you\u2019ll be told two things: Google uses a technology called LSI to index web&nbsp;pages. Using LSI keywords in your content helps you rank higher on Google. Both of these claims are technically<span class=\"ellipsis\">\u2026<\/span><\/p>\n<div class=\"read-more\">Read more \u203a<\/div>\n<p><!-- end of .read-more --><\/p>\n","protected":false},"author":114,"featured_media":33006,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"wp_typography_post_enhancements_disabled":false,"footnotes":""},"categories":[413],"tags":[],"coauthors":[336],"class_list":["post-32993","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-on-page-seo","odd"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>LSI Keywords: What are They and Do They Matter?<\/title>\n<meta name=\"description\" content=\"People say that LSI keywords have the power to boost Google rankings. Is this true, or is it yet another SEO myth?\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ahrefs.com\/blog\/lsi-keywords\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"LSI Keywords: What are They and Do They Matter?\" \/>\n<meta property=\"og:description\" content=\"Could they be the secret to higher rankings?\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ahrefs.com\/blog\/lsi-keywords\/\" \/>\n<meta property=\"og:site_name\" content=\"SEO Blog by Ahrefs\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/Ahrefs\/\" \/>\n<meta property=\"article:published_time\" content=\"2020-01-29T00:30:03+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-10-17T15:55:00+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/ahrefs.com\/blog\/wp-content\/uploads\/2020\/01\/fb-lsi-keywords.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1920\" \/>\n\t<meta property=\"og:image:height\" content=\"990\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Joshua Hardwick\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:title\" content=\"LSI Keywords: What are They and Do They Matter?\" \/>\n<meta name=\"twitter:description\" content=\"Could they be the secret to higher rankings?\" \/>\n<meta name=\"twitter:creator\" content=\"@JoshuaCHardwick\" \/>\n<meta name=\"twitter:site\" content=\"@ahrefs\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/\"},\"author\":{\"name\":\"Joshua Hardwick\",\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/#\\\/schema\\\/person\\\/e6a89cbde8e750d22996aa26e213e712\"},\"headline\":\"LSI Keywords: What are They and Do They Matter?\",\"datePublished\":\"2020-01-29T00:30:03+00:00\",\"dateModified\":\"2024-10-17T15:55:00+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/\"},\"wordCount\":2441,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/wp-content\\\/uploads\\\/2020\\\/01\\\/fb-lsi-keywords.png\",\"articleSection\":[\"On-Page SEO\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/\",\"url\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/\",\"name\":\"LSI Keywords: What are They and Do They Matter?\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/lsi-keywords\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/ahrefs.com\\\/blog\\\/wp-content\\\/uploads\\\/2020\\\/01\\\/fb-lsi-keywords.png\",\"datePublished\":\"2020-01-29T00:30:03+00:00\",\"dateModified\":\"2024-10-17T15:55:00+00:00\",\"description\":\"People say that LSI keywords have the power to boost Google rankings. 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